Large database systems, such as data warehouses used for decision-support and online analytical processing (OLAP) applications, are widespread among corporate information technology (IT) departments. Many companies use enterprise-wide data warehouses to store vast amounts of information on various subjects. The information usually comes from all areas of the company's business and often includes data about the company's operations, its transactions, and its customers. The information in these large database systems is organized physically in a manner that makes efficient use of the hardware and software resources that make up the database. The information is organized logically in a manner that allows the people running the business to understand the nature and content of the data. In general, each company uses one or more logical data models (LDMs) to define the logical relationships among the data stored in the database.
Building and populating a large database is an expensive and time-consuming process. In general, people who understand the logical structure of the database and the applications to be run against the database must prepare a strategy for loading data into the database and bringing the application online. Careful planning allows efficient use of the database resources, in most cases allowing the business to derive benefit from the database even while it is under construction. This type of planning, however, is very time intensive and involves vast amounts of labor by highly skilled personnel. Moreover, this type of planning also generates large amounts of paper, such as word-processing documents and spreadsheet tables that explain the relationships among the components of the database and recommend courses and schedules for bringing the database online.